Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/15485
Title: Hierarchical clustering based greedy routing in vehicular ad hoc networks
Researcher: Jayasudha K
Guide(s): Chandrasekar C
Keywords: Clustering, Intelligent Transportation Systems(ITS), Mobile Ad hoc Networks, Inter-Vehicle Communication, Vehicular Ad-Hoc Netowrks, Wireless Local Area Network, Hierarchical Clustering Based Greedy Routing Approach
Upload Date: 30-Jan-2014
University: Anna University
Completed Date: 
Abstract: Intelligent Transportation Systems (ITS) utilize newly developed information and communication technology in vehicles to improve safety, efficiency, travel time and comforts. They also reduce delay and fuel consumption. As a component of ITS and one of the concrete applications of Mobile Ad hoc Networks (MANETs), Inter-Vehicle Communication (IVC) has attracted research attention from both the academia and the industry. Vehicular Ad-Hoc Networks (VANETs) is a form of MANETs that emerged through IVC and it provides communications among nearby as well as between vehicles without the need of permanent and fixed infrastructure. It is an emerging new technology which allows vehicles to form a self organized network by integrating ad hoc network, Wireless Local Area Network (WLAN) and cellular technology to achieve intelligent inter-vehicle communications. Hierarchical Clustering Based Greedy Routing Approach (HCBGR), a unicast position based greedy routing approach was proposed, which uses the position, speed, direction of motion and link stability of neighbour nodes to select the most suitable next forwarding node. It obtains position, speed and direction of its neighbouring nodes from Global Positioning system (GPS). HCBGR Approach consists of six functional processes, namely Neighbour Node Identification (NNI), Distance Calculation (DC), Direction of Motion Identification (DMI), Reckoning Link Stability (RLS), Weighted score calculation (WS) and Potential Node Selection (PNS). Based on PNS, the HCBGR is fragmented into two important modules (i.e. HCBGR-General Clustering (HCBGR-GC) and HCBGR-Divisive Clustering (HCBGR-DC)). With varied vehicle distance, an average gain of 1 hop and 2 hops is obtained using HCBGR-GC and HCBGR-DC respectively. With varied speeds of vehicles, an average gain of 1 hop is obtained using HCBGR-GC and HCBGR-DC. This thesis presents the detailed description of HCBGR approach. HCBGR approach improves the performance of routing by overcoming the limitations of existing routing prot
Pagination: xx, 144
URI: http://hdl.handle.net/10603/15485
Appears in Departments:Faculty of Science and Humanities

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03_abstract.pdf32.59 kBAdobe PDFView/Open
04_acknowledgement.pdf36.95 kBAdobe PDFView/Open
05_contents.pdf80.05 kBAdobe PDFView/Open
06_chapter 1.pdf203.16 kBAdobe PDFView/Open
07_chapter 1.pdf215.33 kBAdobe PDFView/Open
08_chapter 3.pdf734.28 kBAdobe PDFView/Open
09_chapter 4.pdf608.18 kBAdobe PDFView/Open
10_chapter 5.pdf66.9 kBAdobe PDFView/Open
11_references.pdf78.52 kBAdobe PDFView/Open
12_publications.pdf65.96 kBAdobe PDFView/Open
13_vitae.pdf31.54 kBAdobe PDFView/Open


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